Changelog

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v2 Stateful API

• File management — upload a file with POST /api/v2/files and receive a file://<uuid> URI. Use that URI in any subsequent parse, extract, batch, or classify call instead of re-uploading the file.
• Config management — save a named extraction config with POST /api/v2/configs and reference it as config://<uuid>. Supports parse, extract, and classify config types so you can reuse the same schema or prompt across requests without repeating parameters.
• Parse endpoint — POST /api/v2/parse converts documents to markdown or HTML. Accepts a file upload, a URL, or a file:// URI.
• Extract endpoint — POST /api/v2/extract runs structured extraction and returns JSON or CSV. Accepts a file upload, URL, or file:// URI plus an inline config or config:// URI.
• Classify endpoint — POST /api/v2/classify classifies documents by type with optional split mode that identifies page-level document boundaries within a single multi-page file.
• Batch endpoint — POST /api/v2/batch accepts a list of items, each with its own input and config, and returns a per-item result array in one response.

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Example: Upload then extract

# 1. Upload the file
FILE_ID=$(curl -s -X POST https://api.nanonets.com/api/v2/files \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@invoice.pdf" | jq -r '.file_id')

# 2. Save a config
CONFIG_ID=$(curl -s -X POST https://api.nanonets.com/api/v2/configs \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"name":"Invoice config","type":"extract","params":{"schema":{"invoice_number":"string","total":"number"}}}' \
  | jq -r '.config_id')

# 3. Extract using both URIs
curl -X POST https://api.nanonets.com/api/v2/extract \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d "{\"input\": \"$FILE_ID\", \"config\": \"$CONFIG_ID\"}"

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OCR 3 Engine for JSON Extraction

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Automatic Image Unwarping

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AWS Marketplace

• Consolidated billing — charges appear on your AWS invoice alongside other Marketplace subscriptions.
• Existing Stripe plans unaffected — current Stripe subscribers continue on their existing plan; Marketplace billing is a separate path.
• Same API, same keys — Marketplace accounts use the same API surface and credentials as direct-signup accounts.

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New File Format Support

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MCP Access and Reusable Extraction Workflows

• Connect AI assistants through the hosted MCP server — use OAuth 2.1 sign-in for Claude and other MCP clients, with built-in document extraction and exploration tools.
• Reuse existing uploads by record_id — rerun sync, async, streaming, or batch extraction on previously uploaded files instead of sending the same file again.
• Upload files in one request via /api/v1/upload/file — send multipart form data and get a record_id back immediately for follow-on extraction calls.
• Embed metadata directly in JSON results — use metadata_options=unified to merge bounding boxes and confidence data into the response structure you consume.

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Batch File Organization

• Create and delete batches — give each batch a name and optional description.
• Move files into batches — assign any uploaded file to a batch using the batch picker.
• Filter by batch — the Files page filters to show only files in the selected batch, keeping large workspaces organized.

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Document Navigation

• Page navigation — step through pages of extraction results without returning to the file list.
• JSON viewer improvements — unified metadata fields (value, confidence score, and bounding box) are displayed inline per field. Table results with many rows are paginated at 50 rows per page.

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Larger File Uploads

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TypeScript SDK

• Full type coverage — every request and response is typed, including nested objects, enums, and optional fields.
• Sync and async extraction — call /api/v1/extract/sync, /api/v1/extract/async, /api/v1/extract/batch, and /api/v1/extract/stream with typed parameters.
• File upload helpers — pass a File, Blob, or ReadStream directly; the SDK handles multipart encoding.
• Documentation — full usage guide and examples available in the TypeScript SDK docs.

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Excel Viewer

• In-browser spreadsheet rendering — rows, columns, and cell values are rendered in a spreadsheet-style grid without downloading the file.
• Multi-sheet navigation — documents with multiple sheets show a tab bar; switch between sheets without leaving the results view.
• Integrated with the results page — the viewer loads automatically when the extracted file is a spreadsheet format.

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Excel Extraction Pipeline

• Sheet-aware extraction — each sheet is extracted separately and included in the structured response.
• Preprocessing step — spreadsheet data is normalized before extraction to improve field and table accuracy.
• Compatible with existing options — output_format, json_options, and metadata_options all work with Excel inputs.

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Python SDK

• Sync and async clients — use NanonetsClient for synchronous calls or AsyncNanonetsClient with asyncio.
• Typed models — all request and response objects are Pydantic models with IDE autocompletion.
• File upload support — pass a file path, bytes, or file-like object directly to extraction methods.
• Documentation — full usage guide and examples available in the Python SDK docs.

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AI-Powered Schema Generation from Natural Language

• Natural language input — describe what you want to extract in plain English instead of manually building complex JSON schemas.
• One-click generation — click the new schema generation icon in the schema builder and provide a text prompt to instantly generate a valid JSON schema.
• Automatic validation — generated schemas are validated and can be edited using the visual schema builder or kept as raw JSON.
• Smart parsing — complex schemas with nested objects and arrays are automatically detected and handled appropriately.

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How to Use

1. Navigate to the Configuration page in your extraction workflow
2. In the JSON Options schema builder section, click the new Generate Schema from Prompt icon (layers icon) next to the Paste Schema button
3. Enter a description of your desired schema, for example:

   “I want to extract invoice information including invoice number, date, total amount, customer name, and a list of line items with product name, quantity, and price.”

4. Click Generate Schema and the AI will create a structured JSON schema matching your requirements
5. The generated schema automatically populates the schema builder where you can review, edit, or use it directly

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Example Input

Extract purchase order data with PO number, vendor name,
order date, delivery address, and line items containing
item description, quantity, unit price, and total price

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Generated Schema

{
  "type": "object",
  "properties": {
    "po_number": {
      "type": "string",
      "description": "Purchase order number"
    },
    "vendor_name": {
      "type": "string",
      "description": "Name of the vendor"
    },
    "order_date": {
      "type": "string",
      "format": "date",
      "description": "Date the order was placed"
    },
    "delivery_address": {
      "type": "string",
      "description": "Delivery address"
    },
    "line_items": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "item_description": {
            "type": "string",
            "description": "Description of the item"
          },
          "quantity": {
            "type": "number",
            "description": "Quantity ordered"
          },
          "unit_price": {
            "type": "number",
            "description": "Price per unit"
          },
          "total_price": {
            "type": "number",
            "description": "Total price for this line item"
          }
        },
        "required": [
          "item_description",
          "quantity",
          "unit_price",
          "total_price"
        ],
        "additionalProperties": false
      },
      "description": "List of line items in the purchase order"
    }
  },
  "required": [
    "po_number",
    "vendor_name",
    "order_date",
    "delivery_address",
    "line_items"
  ],
  "additionalProperties": false
}

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Webhooks

• Complete data delivery — the webhook POST payload contains the same result structure as the GET /v1/extract/results/{record_id} API, including all requested formats (markdown, json, csv, html) with metadata.
• New Webhooks page — configure and manage your webhook URL from the dedicated Webhooks page in the sidebar.
• Request context included — the payload includes filename, output_format, pages_processed, processing_time, created_at, and the original request_config.

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Example Webhook Payload

{
  "event": "file.processed",
  "success": true,
  "record_id": "12345",
  "status": "completed",
  "result": {
    "markdown": {
      "content": "# Invoice\n\n| Item | Amount |\n|---|---|\n| Widget | $50.00 |",
      "metadata": {}
    }
  },
  "processing_time": 2.45,
  "filename": "invoice.pdf",
  "output_format": "markdown",
  "pages_processed": 3,
  "created_at": "2026-02-20T10:30:00.000Z"
}

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Document Classification API

• New /api/v1/classify/sync endpoint — classify a single document and get category, document type, and explanation in one response.
• New /api/v1/classify/batch endpoint — classify up to 50 files per request for higher-throughput document triage.
• Routing-ready output — classification labels make it easier to route documents to downstream extraction or review flows.

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Bounding Boxes in JSON Responses

• Per-field bounding boxes — each extracted field in the JSON output includes a bounding box mapping back to its location in the source document.
• Multi-page support — page-aware resolution of bounding boxes.

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Example Request

curl -X POST https://api.nanonets.com/api/v1/extract/sync \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@invoice.pdf" \
  -F "output_format=json" \
  -F 'json_options=["invoice_number","total","vendor"]' \
  -F "include_metadata=confidence_score,bounding_boxes"

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Example Response

{
  "result": {
    "json": {
      "content": {
        "invoice_number": "INV-2024-001",
        "total": 1250.00,
        "vendor": "Acme Corp"
      },
      "metadata": {
        "confidence_score": { "invoice_number": 98, "total": 95, "vendor": 92 },
        "bounding_boxes": {
          "invoice_number": { "page": 1, "x": 0.62, "y": 0.08, "width": 0.15, "height": 0.02, "confidence": 0.99 },
          "total": { "page": 1, "x": 0.70, "y": 0.85, "width": 0.12, "height": 0.02, "confidence": 0.98 },
          "vendor": { "page": 1, "x": 0.10, "y": 0.12, "width": 0.18, "height": 0.02, "confidence": 0.97 }
        }
      }
    }
  }
}

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Word-Level Bounding Boxes

• Block-level (include_metadata=bounding_boxes) — Existing behavior. Returns one bounding box per paragraph or region detected by layout analysis. Ideal for highlighting sections, table rows, or paragraphs.
• Word-level (include_metadata=bounding_boxes_word) — New. Returns one bounding box per individual word using advanced OCR. Enables fine-grained annotation, search-hit highlighting, and word-by-word document overlays.

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Response Structure

{
  "result": {
    "markdown": {
      "content": "# Invoice\n\n| Date | Amount |\n| --- | --- |\n| 12/24/18 | $44.78 |",
      "metadata": {
        "bounding_boxes": {
          "success": true,
          "bounding_box_type": "word",
          "elements": [
            {
              "id": "word_0",
              "content": "Invoice",
              "type": "word",
              "page": 1,
              "markdown_line": 0,
              "word_offset": 0,
              "bounding_box": {
                "x": 0.695,
                "y": 0.055,
                "width": 0.051,
                "height": 0.007,
                "confidence": 0.99,
                "page": 1,
                "normalized": true
              }
            }
          ],
          "original_markdown": "# Invoice\n\n| Date | Amount |\n| --- | --- |\n| 12/24/18 | $44.78 |",
          "page_dimensions": { "pages": [{ "page": 1, "width": 1604, "height": 2048 }], "total_pages": 1 }
        }
      }
    }
  }
}

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API Playground UI

• Output format selection - Choose from Markdown, JSON, CSV/Excel, or HTML output
• Schema Builder - Visual JSON schema editor with support for nested objects, arrays, and enums up to 10 levels deep
• Field List mode - Quick extraction with simple field name arrays
• Metadata options - Enable confidence scores and bounding boxes per field

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Streaming Extraction Endpoint

• Streaming mode - Content delivered in small chunks as it’s generated
• Batch mode - Content sent all at once when extraction completes

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v1 Extraction API

• Sync extraction (/v1/extract/sync) - Process documents synchronously with immediate results
• Async extraction (/v1/extract/async) - Queue documents for background processing
• Batch extraction (/v1/extract/batch) - Process up to 50 files in a single request
• Fetch result (/v1/extract/results/<record_id>) - Fetch the results for a single record_id
• Results endpoints (/v1/extract/results) - List and retrieve extraction results with pagination

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Multi-Page JSON Extraction with Confidence Scoring

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Bounding Box Extraction API

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Response Dimensions

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Streaming & Partial Results

• New streaming extraction endpoint at /v1/extract/stream
• Partial results API to retrieve in-progress extractions
• Improved delimiter handling for chunked responses

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Billing & Usage APIs

• Credit usage reporting integration with Stripe
• Subscription status tracking
• Document processing limits per plan

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On-Premise License APIs

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Repetition Detection & Retry

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Excel & DOCX Processing

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OpenAI-Compatible Chat Completions API

• PDF and document uploads directly in requests
• All major file types (PDF, Excel, Word, images)
• Drop-in replacement for OpenAI SDK integrations

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Hierarchy Extraction API

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Custom Prompt Instructions

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Expanded File Type Support

• PDF documents
• Excel spreadsheets (.xlsx, .xls)
• Word documents (.docx)
• All major image formats

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OpenAI SDK Upgrade